{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:54:21Z","timestamp":1777704861722,"version":"3.51.4"},"reference-count":16,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Aspect-based sentiment analysis is a fine-grained task in the field of sentiment analysis. Various GCN approaches have recently emerged to work on this, but many approaches ignored the critical role of aspectual word information and the effect of noise. In view of this situation, we propose an aspect-based word embedding graph convolutional network (AWEGCN) model. In order to make good use of the aspect information and distinguish the contextual information that is more important for a particular aspect, the aspect information is embedded in the output of the hidden layer. To reduce the noise effect when multiple aspect words appear in a sentence, after going through the bidirectional graph convolutional network, the aspect information is embedded. A specific contextual representation is computed through an attention mechanism, which is used as the final classification feature. Experiments show that our model achieves impressive performance on five public datasets, and we also apply BERT and XLNet pre-trained models to this task and obtain advanced results that validate the effectiveness of our model.<\/jats:p>","DOI":"10.3233\/jifs-230537","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T10:50:18Z","timestamp":1697799018000},"page":"11949-11962","source":"Crossref","is-referenced-by-count":0,"title":["Research on sentiment analysis methods based on aspect word embedding graph convolutional networks"],"prefix":"10.1177","volume":"45","author":[{"given":"Qiuyue","family":"Wei","sequence":"first","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, China"},{"name":"School of Automation, Xi\u2019an Robertic Intelligent Systems International Science and Technology Cooperation Base, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}]},{"given":"Dong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, China"}]},{"given":"Mingjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-230537_ref2","doi-asserted-by":"crossref","first-page":"107643","DOI":"10.1016\/j.knosys.2021.107643","article-title":"Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks","volume":"235","author":"Liang","year":"2022","journal-title":"Knowledge-Based Systems"},{"issue":"5","key":"10.3233\/JIFS-230537_ref10","doi-asserted-by":"crossref","first-page":"8600","DOI":"10.1609\/aaai.v34i05.6383","article-title":"Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis","volume":"34","author":"Peng","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-230537_ref11","doi-asserted-by":"crossref","first-page":"110402","DOI":"10.1109\/ACCESS.2022.3214233","article-title":"Aspect-Level Sentiment Analysis Using CNN Over BERT-GCN","volume":"10","author":"Phan","year":"2022","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-230537_ref12","first-page":"4171","article-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","volume":"1","author":"Devlin","year":"2019","journal-title":"In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"},{"key":"10.3233\/JIFS-230537_ref14","first-page":"49","article-title":"Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification","volume":"2","author":"Dong","year":"2014","journal-title":"In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics"},{"key":"10.3233\/JIFS-230537_ref15","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.10.091","article-title":"Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks","volume":"471","author":"Xiao","year":"2021","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-230537_ref16","first-page":"1725","article-title":"Simple and deep graph convolutional networks","author":"Chen","year":"2020","journal-title":"In Proceedings of the 37th International Conference on Machine Learning (ICML\u201920)"},{"key":"10.3233\/JIFS-230537_ref17","first-page":"6861","article-title":"Differentiable Ranks and Sorting Using Optimal Transport","author":"Cuturi","year":"2019","journal-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems"},{"key":"10.3233\/JIFS-230537_ref18","first-page":"27","article-title":"SemEval-Task 4: Aspect Based Sentiment Analysis","author":"Pontiki","year":"2014","journal-title":"In Proceedings of the 8th International Workshop on Semantic Evaluation"},{"key":"10.3233\/JIFS-230537_ref22","doi-asserted-by":"crossref","first-page":"105443","DOI":"10.1016\/j.knosys.2019.105443","article-title":"Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification","volume":"193","author":"Zhao","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/JIFS-230537_ref24","first-page":"1766","article-title":"Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization","volume":"3","author":"Zhu","year":"2021","journal-title":"35th Conference on Neural Information Processing Systems"},{"issue":"4","key":"10.3233\/JIFS-230537_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJIRR.2017100101","article-title":"Offline vs. Online Sentiment Analysis: Issues With Sentiment Analysis of Online Micro-Texts","volume":"7","author":"Srivastava","year":"2017","journal-title":"International Journal of Information Retrieval Research"},{"key":"10.3233\/JIFS-230537_ref27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"issue":"01","key":"10.3233\/JIFS-230537_ref33","first-page":"6714","article-title":"A Unified Model for Opinion Target Extraction and Target Sentiment Prediction","volume":"33","author":"Li","year":"2019","journal-title":"Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence"},{"key":"10.3233\/JIFS-230537_ref37","first-page":"1137","article-title":"A Neural Probabilistic Language Model","volume":"3","author":"Bengio","year":"2003","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/JIFS-230537_ref40","unstructured":"Yang Z. , Dai Z. , Yang Y. , Carbonell J. , Salakhutdinov R. and Le V. , XLNet: Generalized Autoregressive Pretraining for Language Understanding, Advances in Neural Information Processing Systems 32 (2019)."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-230537","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:11Z","timestamp":1777455731000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-230537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":16,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-230537","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}